Add optimizer for qat demo (#1726)

example/quantization/ptq/classification/ptq.py

@@ -187,7 +187,8 @@ def main():
 
     dummy_input = paddle.static.InputSpec(
         shape=[None, 3, 224, 224], dtype='float32')
-    paddle.jit.save(infer_model, "./int8_infer", [dummy_input])
+    save_path = os.path.join(FLAGS.output_dir, "int8_infer")
+    paddle.jit.save(infer_model, save_path, [dummy_input])
 
 
 if __name__ == '__main__':

example/quantization/qat/classification/optimizer.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+
+
+def piecewise_decay(net, device_num, args):
+    step = int(
+        math.ceil(float(args.total_images) / (args.batch_size * device_num)))
+    bd = [step * e for e in args.step_epochs]
+    lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)]
+    learning_rate = paddle.optimizer.lr.PiecewiseDecay(
+        boundaries=bd, values=lr, verbose=False)
+    optimizer = paddle.optimizer.Momentum(
+        parameters=net.parameters(),
+        learning_rate=learning_rate,
+        momentum=args.momentum_rate,
+        weight_decay=paddle.regularizer.L2Decay(args.l2_decay))
+    return optimizer, learning_rate
+
+
+def cosine_decay(net, device_num, args):
+    step = int(
+        math.ceil(float(args.total_images) / (args.batch_size * device_num)))
+    learning_rate = paddle.optimizer.lr.CosineAnnealingDecay(
+        learning_rate=args.lr, T_max=step * args.num_epochs, verbose=False)
+    optimizer = paddle.optimizer.Momentum(
+        parameters=net.parameters(),
+        learning_rate=learning_rate,
+        momentum=args.momentum_rate,
+        weight_decay=paddle.regularizer.L2Decay(args.l2_decay))
+    return optimizer, learning_rate
+
+
+def create_optimizer(net, device_num, args):
+    if args.lr_strategy == "piecewise_decay":
+        return piecewise_decay(net, device_num, args)
+    elif args.lr_strategy == "cosine_decay":
+        return cosine_decay(net, device_num, args)